{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tutorial: Plotting VIC Model Output\n",
    "====\n",
    "\n",
    "This Jupyter Notebook outlines one approach to plotting VIC output from the classic and image drivers. The tools used here are all freely available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jhamman/anaconda/lib/python3.4/site-packages/pandas/computation/__init__.py:19: UserWarning: The installed version of numexpr 2.4.4 is not supported in pandas and will be not be used\n",
      "\n",
      "  UserWarning)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# input files for example: \n",
    "asci_fname = '/Users/jhamman/workdir/VIC_tests_20160531/examples/Example-Classic-Stehekin-fewb/results/fluxes_48.1875_-120.6875.txt'\n",
    "nc_fname = '/Users/jhamman/workdir/VIC_tests_20160331/examples/Example-Image-Stehekin-base-case/results/Stehekin.history.nc'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Classic Driver Output\n",
    "\n",
    "#### Reading VIC ASCII data:\n",
    "We'll use pandas to parse the ASCII file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OUT_PREC</th>\n",
       "      <th>OUT_EVAP</th>\n",
       "      <th>OUT_RUNOFF</th>\n",
       "      <th>OUT_BASEFLOW</th>\n",
       "      <th>OUT_WDEW</th>\n",
       "      <th>OUT_SOIL_LIQ_0</th>\n",
       "      <th>OUT_SOIL_LIQ_1</th>\n",
       "      <th>OUT_SOIL_LIQ_2</th>\n",
       "      <th>OUT_RAD_TEMP</th>\n",
       "      <th>OUT_SWNET</th>\n",
       "      <th>...</th>\n",
       "      <th>OUT_GRND_FLUX</th>\n",
       "      <th>OUT_DELTAH</th>\n",
       "      <th>OUT_FUSION</th>\n",
       "      <th>OUT_AERO_RESIST</th>\n",
       "      <th>OUT_SURF_TEMP</th>\n",
       "      <th>OUT_ALBEDO</th>\n",
       "      <th>OUT_REL_HUMID</th>\n",
       "      <th>OUT_IN_LONG</th>\n",
       "      <th>OUT_AIR_TEMP</th>\n",
       "      <th>OUT_WIND</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEAR_MONTH_DAY</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1949-01-01</th>\n",
       "      <td>4.0488</td>\n",
       "      <td>0.5127</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.3664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1074.9289</td>\n",
       "      <td>1075.4402</td>\n",
       "      <td>715.2995</td>\n",
       "      <td>260.8294</td>\n",
       "      <td>5.9157</td>\n",
       "      <td>...</td>\n",
       "      <td>-22.3248</td>\n",
       "      <td>-22.5923</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118.1365</td>\n",
       "      <td>-9.1150</td>\n",
       "      <td>0.8158</td>\n",
       "      <td>69.7493</td>\n",
       "      <td>220.0001</td>\n",
       "      <td>-9.5276</td>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-02</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.1515</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3656</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1074.4431</td>\n",
       "      <td>1075.4388</td>\n",
       "      <td>713.6348</td>\n",
       "      <td>254.7404</td>\n",
       "      <td>16.9485</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.1654</td>\n",
       "      <td>-3.7829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.8241</td>\n",
       "      <td>-16.9640</td>\n",
       "      <td>0.6510</td>\n",
       "      <td>59.2600</td>\n",
       "      <td>177.1149</td>\n",
       "      <td>-13.4942</td>\n",
       "      <td>2.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-03</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0038</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3647</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1073.9601</td>\n",
       "      <td>1075.4364</td>\n",
       "      <td>711.9741</td>\n",
       "      <td>249.6297</td>\n",
       "      <td>12.8363</td>\n",
       "      <td>...</td>\n",
       "      <td>-11.2951</td>\n",
       "      <td>-6.3547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>249.7898</td>\n",
       "      <td>-22.1400</td>\n",
       "      <td>0.6489</td>\n",
       "      <td>76.5877</td>\n",
       "      <td>185.1597</td>\n",
       "      <td>-18.0142</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-04</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0040</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3639</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1073.4798</td>\n",
       "      <td>1075.4332</td>\n",
       "      <td>710.3173</td>\n",
       "      <td>254.1290</td>\n",
       "      <td>7.7933</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.0562</td>\n",
       "      <td>0.5830</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.7634</td>\n",
       "      <td>-18.4670</td>\n",
       "      <td>0.6483</td>\n",
       "      <td>85.9480</td>\n",
       "      <td>210.8265</td>\n",
       "      <td>-17.1740</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949-01-05</th>\n",
       "      <td>0.4488</td>\n",
       "      <td>-0.0547</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.3630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1073.0022</td>\n",
       "      <td>1075.4290</td>\n",
       "      <td>708.6644</td>\n",
       "      <td>255.7232</td>\n",
       "      <td>4.6821</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.5058</td>\n",
       "      <td>1.6561</td>\n",
       "      <td>0.0</td>\n",
       "      <td>279.0023</td>\n",
       "      <td>-16.6536</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>87.9445</td>\n",
       "      <td>216.4266</td>\n",
       "      <td>-14.2423</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                OUT_PREC  OUT_EVAP  OUT_RUNOFF  OUT_BASEFLOW  OUT_WDEW  \\\n",
       "YEAR_MONTH_DAY                                                           \n",
       "1949-01-01        4.0488    0.5127      0.0002        0.3664       0.0   \n",
       "1949-01-02        0.0000    0.1515      0.0000        0.3656       0.0   \n",
       "1949-01-03        0.0000    0.0038      0.0000        0.3647       0.0   \n",
       "1949-01-04        0.0000   -0.0040      0.0000        0.3639       0.0   \n",
       "1949-01-05        0.4488   -0.0547      0.0000        0.3630       0.0   \n",
       "\n",
       "                OUT_SOIL_LIQ_0  OUT_SOIL_LIQ_1  OUT_SOIL_LIQ_2  OUT_RAD_TEMP  \\\n",
       "YEAR_MONTH_DAY                                                                 \n",
       "1949-01-01           1074.9289       1075.4402        715.2995      260.8294   \n",
       "1949-01-02           1074.4431       1075.4388        713.6348      254.7404   \n",
       "1949-01-03           1073.9601       1075.4364        711.9741      249.6297   \n",
       "1949-01-04           1073.4798       1075.4332        710.3173      254.1290   \n",
       "1949-01-05           1073.0022       1075.4290        708.6644      255.7232   \n",
       "\n",
       "                OUT_SWNET    ...     OUT_GRND_FLUX  OUT_DELTAH  OUT_FUSION  \\\n",
       "YEAR_MONTH_DAY               ...                                             \n",
       "1949-01-01         5.9157    ...          -22.3248    -22.5923         0.0   \n",
       "1949-01-02        16.9485    ...           -9.1654     -3.7829         0.0   \n",
       "1949-01-03        12.8363    ...          -11.2951     -6.3547         0.0   \n",
       "1949-01-04         7.7933    ...           -6.0562      0.5830         0.0   \n",
       "1949-01-05         4.6821    ...           -4.5058      1.6561         0.0   \n",
       "\n",
       "                OUT_AERO_RESIST  OUT_SURF_TEMP  OUT_ALBEDO  OUT_REL_HUMID  \\\n",
       "YEAR_MONTH_DAY                                                              \n",
       "1949-01-01             118.1365        -9.1150      0.8158        69.7493   \n",
       "1949-01-02              62.8241       -16.9640      0.6510        59.2600   \n",
       "1949-01-03             249.7898       -22.1400      0.6489        76.5877   \n",
       "1949-01-04              82.7634       -18.4670      0.6483        85.9480   \n",
       "1949-01-05             279.0023       -16.6536      0.8500        87.9445   \n",
       "\n",
       "                OUT_IN_LONG  OUT_AIR_TEMP  OUT_WIND  \n",
       "YEAR_MONTH_DAY                                       \n",
       "1949-01-01         220.0001       -9.5276      4.49  \n",
       "1949-01-02         177.1149      -13.4942      2.66  \n",
       "1949-01-03         185.1597      -18.0142      0.64  \n",
       "1949-01-04         210.8265      -17.1740      2.01  \n",
       "1949-01-05         216.4266      -14.2423      1.88  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the pandas read_table function to read/parse the VIC output file\n",
    "df = pd.read_table(asci_fname, comment='#', sep=r\"\\s*\", engine='python', \n",
    "                   parse_dates=[[0, 1, 2]], index_col='YEAR_MONTH_DAY')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot 1: Time Series of Classic Driver Variables\n",
    "Here we'll use `pandas`' built in plotting to plot 3 of the variables in the dataframe (`df`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x108cb0ba8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6nHWW1QRNKQ+tl9undWvo2hXeecfpSJQvIjaZg5ZaVFnFxVadV+vl9rn1Vvj3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\nscqPZM/ZfdNDsH9laL8pxLl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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1071cae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the precipitation, evapotranspiration, and runoff variables and plot their timeseries.\n",
    "df[['OUT_PREC', 'OUT_EVAP', 'OUT_RUNOFF']].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Image Driver Output\n",
    "The image driver outputs netCDF files.  Here we'll use the `xarray` package to open the dataset, and make a few plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (lat: 4, lon: 5, nlayer: 3, time: 240)\n",
       "Coordinates:\n",
       "  * time            (time) datetime64[ns] 1949-01-01 1949-01-01T01:00:00 ...\n",
       "  * lon             (lon) float64 -121.1 -120.9 -120.8 -120.7 -120.6\n",
       "  * lat             (lat) float64 48.19 48.31 48.44 48.56\n",
       "  * nlayer          (nlayer) int64 0 1 2\n",
       "Data variables:\n",
       "    OUT_SOIL_MOIST  (time, nlayer, lat, lon) float64 nan 15.44 15.44 15.44 ...\n",
       "    OUT_SWE         (time, lat, lon) float64 nan 0.09958 0.008753 0.001741 ...\n",
       "    OUT_BASEFLOW    (time, lat, lon) float64 nan 0.01529 0.01529 0.01529 nan ...\n",
       "    OUT_EVAP        (time, lat, lon) float64 nan 0.06577 0.02227 0.008182 ...\n",
       "    OUT_PREC        (time, lat, lon) float64 nan 0.4312 0.1531 0.1688 nan ...\n",
       "    OUT_RAINF       (time, lat, lon) float64 nan 0.0 0.0 0.0 nan 0.0 0.0 0.0 ...\n",
       "    OUT_RUNOFF      (time, lat, lon) float64 nan 0.0 0.0 0.0 nan 0.0 0.0 0.0 ...\n",
       "    OUT_SNOWF       (time, lat, lon) float64 nan 0.4312 0.1531 0.1688 nan ...\n",
       "    OUT_ALBEDO      (time, lat, lon) float64 nan 0.6251 0.1563 0.03036 nan ...\n",
       "    OUT_SOIL_TEMP   (time, nlayer, lat, lon) float64 nan -2.984 -2.622 ...\n",
       "    OUT_AIR_TEMP    (time, lat, lon) float64 nan -11.75 -10.54 -10.33 nan ...\n",
       "    OUT_DENSITY     (time, lat, lon) float64 nan 1.056 1.044 1.08 nan 1.076 ...\n",
       "    OUT_LWDOWN      (time, lat, lon) float64 nan 213.0 215.7 217.9 nan 223.5 ...\n",
       "    OUT_PRESSURE    (time, lat, lon) float64 nan 79.77 79.2 81.99 nan 81.49 ...\n",
       "    OUT_QAIR        (time, lat, lon) float64 nan 0.001455 0.001626 0.001602 ...\n",
       "    OUT_REL_HUMID   (time, lat, lon) float64 nan 84.25 83.88 83.93 nan 86.96 ...\n",
       "    OUT_SWDOWN      (time, lat, lon) float64 nan 0.0 0.0 0.0 nan 0.0 0.0 0.0 ...\n",
       "    OUT_VP          (time, lat, lon) float64 nan 0.1866 0.2071 0.2112 nan ...\n",
       "    OUT_VPD         (time, lat, lon) float64 nan 0.0349 0.03979 0.04044 nan ...\n",
       "    OUT_WIND        (time, lat, lon) float64 nan 2.0 2.0 2.0 nan 2.0 2.0 2.0 ...\n",
       "Attributes:\n",
       "    title: VIC History File\n",
       "    source: VIC Image Driver\n",
       "    history: Created by jhamman on D-173-250-163-133.dhcp4.washington.edu on Tue May 17 11:51:44 2016\n",
       "\n",
       "    references: Primary Historical Reference for VIC: Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415-14,428.\n",
       "    comment: Output from the Variable Infiltration Capacity (VIC)Macroscale Hydrologic Model\n",
       "    Conventions: CF-1.6\n",
       "    VIC_Model_Version: 5.0 beta 2016 April 25\n",
       "    VIC_Driver: Image"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Open the dataset\n",
    "ds = xr.open_dataset(nc_fname)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot 2: Time slice of image driver output\n",
    "Quick and simple, select a time slice of the EVAP variable and plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x10c75b4e0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jhamman/anaconda/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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DGBavfckLeO1Lnvls+PL/u3FY2m23h1MOoDoLeDPFrYRbJc2xvbChzGzgxbanSdob+CYw\nY5C6JwPzbJ8h6aTy+GTgcGCC7VdI2gxYIOkHtoc/C1fUsb6h7VNsT7W9E3Ak8FPbRwMPStqvLPZG\nYHH/upKeJWmLcn9z4ABgfqdijYioagj3cPYCltheansVcBFwaL8yhwDnA9i+BZgkadtB6q6tU/55\nWLnfC2xeJqvNgb9SjPwdMd0cbuHyz/cDZ0raBHgK+ACApMnAt20fCGwL/EhSX4wX2L66i7FGRDQ1\nhKlttgeWNRwvB/auUGZ7YPIAdbexvaLcXwFsU+5fQpF8fgc8i+JWxqPtBj8cupJwbF8HXFfu3wS8\nukmZB4EDy/3fAtO7EVtERB3jWgyLvmnJMm5asnygqh7owQZVRueqWXu2Lanv/N4Uv3XcDtgKuEHS\nNbbvrRjHsMuA8oiIGlrdw+l/z+grV9/Sv8gDwNSG46kUPZWBykwpy4xvcr7vJyUrJG1r+yFJ2wEr\ny/N/B1xpew3wsKS+L/sjlnAyvi8iogaNG1dpa+I2YJqkHSVNAI4A5vQrMwd4F4CkGcCj5eWygerO\nAY4p948Bflzu30dxn7zvXvgMYO0AhZGQHk5ERA3j2pzaxvZqSccDV1EMbT7X9kJJx5aPn2P7Ckmz\nJS0B/gy8Z6C6ZdOnAxdLei/lsOjy/P8FvivplxSX4M6z/cu2gh8mSTgRETUM5YeftucCc/udO6ff\n8fFV65bnH6EYLt3//J95JvlsEJJwIiJqyEwD7UvCiYioodUotRhc3rmIiBrSw2lfEk5ERA1ZgK19\nSTgRETVoXBZga1cSTkREHUk4bUvCiYioI5fU2paEExFRg9r84Wck4URE1LPJhJGOYNRKwomIqCGj\n1NqXhBMRUUcGDbQtCScioo4knLYl4URE1JBLau1LwomIqCM9nLYl4URE1JGE07YknIiIGjR+/EiH\nMGol4URE1JEeTtty9ysiogaN66m0Na0rzZK0SNJvJJ3UoszXy8fvlrT7YHUlbSVpnqTFkq6WNKlf\neztIekLSx4bpLWhbEk5ERB3jxlXb+pHUA5wFzAJ2BY6StEu/MrOBF9ueBnwA+GaFuicD82zvDFxT\nHjf6Z+Dy4XnxQ5OEExFRwxB6OHsBS2wvtb0KuAg4tF+ZQ4DzAWzfAkyStO0gddfWKf88bG2s0mHA\nb4EFw/HahyoJJyKijnE91bb1bQ8sazheXp6rUmbyAHW3sb2i3F8BbAMg6dnA/wJOq/0aOySDBiIi\n6mjxw8/rbr2b6267Z6CarvgMqlhmvfZsW1Lf+dOAr9p+UlKVNjsuCSciogaNbz5b9Mx99mTmPnuu\nPf7cOd/vX+QBYGrD8VSKnspAZaaUZcY3Of9Aub9C0ra2H5K0HbCyPL8X8HZJZwCTgF5JT9k+e8AX\n2EG5pBYRUUf7l9RuA6ZJ2lHSBOAIYE6/MnOAdwFImgE8Wl4uG6juHOCYcv8Y4McAtl9veyfbOwFf\nA74w1GQjaeuh1E8PJyKihnbnUrO9WtLxwFVAD3Cu7YWSji0fP8f2FZJmS1oC/Bl4z0B1y6ZPBy6W\n9F5gKfCO9l9dc5IOBs4DVktaAxxh+6a67SThRETUMYQfftqeC8ztd+6cfsfHV61bnn8EePMgz/vZ\n2sGu64vA62wvkrQ38E/A6+s2koQTEVGHxuSdiNW2F0ExXFvSFu00koQTEVHH2Ew4z5P0jzwzgq7x\n2Lb/uUojSTgRETV4bCac7wBbNDluOjy7lSSciIg6xuDknbZPa/WYpL2qtpOEExFRR1b8RNJuwFHA\nkcCjwKur1EvCiYioYYxeUkPSThQJ5ihgFfAC4NW2l1Zto+PvnKQeSXdKuqw8ni7p5vLcrZL2rFo3\nImLEaVy1bSMi6efA/6OY8eDttl8FPF4n2UB3Zho4gWKm0r4bS2cAp9reHfhMeVy1bkTEyBqDCYdi\nUtAtKCYGfX67jXT0XZE0BZhNMaKhbzhdLzCx3J/EM/MBVakbETGyxmDCsX0Y8ArgduBUSfcCzyl/\nBFpZp+/hfBX4BLBlw7kTgSslfYUi4b2mRt2IiBHlcWPv1rek8bYfpZje5jxJ21BMofNVSVNtTx24\nhULH3jlJBwErbd8paWbDQx8CPmr7UkmHU7yA/SvWberc++9bu7/7xInsMXHSAKUjYiy48df3cePi\n+4e/4Q1jpv9ue0DSHOBC4KflhKLfAL4haceqjcjuzO0RSV8EjgZWA5tS9FR+BBxse1JZRhSzoU6s\nUPeHtt/V5Hl8476v7chrGCm33bNy8EKjzAd+8JGRDqEjtMn4kQ5h2D19/9KRDqEjnnPsl7A9pGwh\nyX9ded/gBYEJz3/BkJ9vQ1HOEv23FKPUpgGXABfavrlOOx270Gj7FNtTy6mxj6TIikcDD0raryz2\nRmBxxbrrJZuIiG6zxlXaNia2f2/7W7ZnAnsC91JcTvvvsoNQSTfflb6u1PuB/yPpLuDzwAcAJE2W\ndPkgdSMiRta4cdW2jZTtB4FzgW8BTwDvq1q3K3e/bF8HXFfu30STX6WWL+LAgepGRIy4jaz3UpWk\nzYCDKa467QtcCZwE/KRqG2NvuEVExFCMwYQj6QcUg7uuAy4A/t72U3XbScKJiKhhLA6LpujNHGv7\n8aE0MmiqlvTlKuciIsaEIfzwU9IsSYsk/UbSSS3KfL18/G5Juw9WV9JWkuZJWizpakmTGh77ZFl+\nkaQDhvCqX9WXbCSd0C/ef63aSJW+YbMgZ1d9goiIjYpUbVuvmnqAs4BZwK7AUZJ26VdmNvBi29Mo\nBlR9s0Ldk4F5tncGrimPkbQrcERZfhZwttT29cDG5aTf3e+xV1ZtpOWTS/qQpPnASyTNb9iWAvfU\niTQiYqPRfg9nL2CJ7aW2VwEXAYf2K3MIcD4USzkDkyRtO0jdtXXKPw8r9w+l+K3MqnKSzSVlOyNm\noIuRPwDmAqdTjEToS9mP2/5DpwOLiNgQDeE3NtsDyxqOlwP95yJrVmZ7YPIAdbcpf/kPxSSb25T7\nk4Gb+9XZvs3YeyRtRZEH+vbpO67aSMuEY/sx4DGKIXBIej7Fr/43l7S57Q7MGRERsYFrkXCuv/56\nrr/++oFqVv09YZXZCZou7WzbkgZ6nnZ/07glxcSdfc99+wBlWxp0uIWkQ4D/Q5EtV1IsurMQ2K2d\nJ+wEff/HIx3CsPqH678x0iEMu/ln/XCkQ+iI86767UiHMOy+/J13jnQIGzS3mEvtdfvtx+v222/t\n8Re+uN4P8B8AGie5nErR6xiozJSyzPgm5/tm2l8haVvbD0najuJzulVbTWfnH4ztHauUk7Sb7V+1\nerxK3/DzFDM6Ly6nmnkTcEuVJ4+I2Nis6XWlrYnbgGmSdpQ0geKG/px+ZeYA7wKQNINirskVg9Sd\nAxxT7h8D/Ljh/JGSJpSrdU4DfjEc78EAvj/Qg1UGlK+y/XtJ4yT12P6ZpDOHKbiIiFGl3WtStldL\nOh64iuK+x7m2F0o6tnz8HNtXSJotaQnwZ+A9A9Utmz4duFjSe4GlFMsGYHuBpIspFrFcDRznTs3W\nXFGVhPNHSVsANwAXSFpJMX9ORMSY07zzUo3tuRSDsRrPndPv+PiqdcvzjwBvblHni0DlyTU7rcol\ntcOAJykXTqMYWndwJ4OKiNhQ2a60xfoG7eHY7uvNrAH+taPRRERs4IbSwxmtJO1QcWTyXwZ6sGXC\nkfQErS9X2naWfo6IMWcM5huA/wR2H6yQ7RkDPT7Q73Ce3UZQEREbtbHYwxkuY3La04iIdq0Zm/dn\ntpf0dZr/KNW2K60hn4QTEVHD2Mw3PEUxu0D/GQ6aznjQShJOREQNY/SS2iO2zx+82MCScCIiahij\nQ54HHH1WVRJOREQNvSMdwMj4sKQ9Go4N/N72slYVmknCiYioYWx2cPhKk3NblfO6HWX7riqNJOFE\nRNTQOwYzju03NDsv6dXA11l3RdCWknAiImpYM/byTUu2byvn2qwkCSciooYx2MFpSdI21LitlYQT\nEVFD7xic3EZSs1UhnwPsC5xQtZ0knIiIGsZoD+d2ipFpz6LIG88Brgb+0fbKgSo2SsKJiKhhjP7w\n8wLgC8A/AH2zRn8E+K6kU2yvqtJIlfVwIiKiZFfbNjL/BGwF7GR7D9t7AC8EJtF8yHRTSTgRETWs\nsSttdUjaStI8SYslXS1pUotysyQtkvQbSSdVqS/pk2X5RZIOaNLmHEnzBwnxIOADth/vO2H7T8AH\ngQOrvs4knIiIGnrtSltNJwPzbO8MXFMer0NSD3AWMAvYFThK0i4D1Ze0K3BEWX4WcLakcQ1tvg14\nnMEn4Oy1vd5oNNtrqDFKLQknIqKGNb3VtpoOAfomxzwfOKxJmb2AJbaXlvdMLgIOHaT+ocCFtlfZ\nXgosKdtB0rOBE4HP03zZgUYLJR3T/6Sko4FFg766UgYNRETU0KGZBraxvaLcXwFs06TM9kDj3GXL\ngb0HqT8ZuLlfncnl/uco7r88WSG+DwM/kvQPFCPWAF5FMWrtrRXqA0k4ERG1tLo/c8fPb+SOm29q\nWU/SPGDbJg99qvHAtiU1e5L+55quRTNA/YZQNB14oe0TJe04QNm+NpdL2ht4I7Bb+byX275msLqN\nknAiImpo1cOZPmNfps/Yd+3xuWeesc7jtvdv1aakFZK2tf2QpO2AZr9teQCY2nA8pTwH0Kp+szrL\ngRnAqyXdS5EHni/pp7bf2CpGF+syXFNubck9nIiIGjp0D2cO0HeP5Bjgx03K3AZMk7RjOUvzEWW9\ngerPAY6UNEHSTsA04Be2v2V7e9s7Aa8FFg+UbIZLxxOOpB5Jd0q6rDyeLunm8tytkvZsUmdTSbdI\nukvSLyWd1uk4IyK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c70f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ds['OUT_EVAP'].sel(time='1949-01-04-00').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot 3: Multiple time slices of image driver output\n",
    "Xarray allows you to plot multiple time periods at once, here we plot the daily average SWE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x10c7bccc0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jhamman/anaconda/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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Fp0WUWrGI2jRPAfM2ol0Aquqp9M5r/tWquqz5tubVVXXL2I2r6lqm4RujJIvo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\nXQ28ulnnMOAFzfjX6d0ytbSqjkkyO8k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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c7b84a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['OUT_SWE'].resample('1D', dim='time', how='mean').plot(col='time', col_wrap=4, levels=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot 4: Multiple time slices of 4d image driver output\n",
    "For 4d variables, we can again use the xarray facet grid, now time is along the x axis and soil layer is along the y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x10db06470>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jhamman/anaconda/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10de155c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['OUT_SOIL_TEMP'].sel(time='1949-01-04').resample(\n",
    "    '3h', dim='time', how='mean').plot(\n",
    "        col='time', row='nlayer', levels=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot 5: Using Cartopy to project VIC Image Driver Output\n",
    "Often, we want to plot maps of VIC output that are georeferenced and include things like coastlines and political boundaries.  Here we use `xaray` plotting along with `cartopy` to plot the temporal mean evapotranspiration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jhamman/anaconda/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n",
      "/Users/jhamman/anaconda/lib/python3.4/site-packages/numpy/lib/shape_base.py:431: FutureWarning: in the future np.array_split will retain the shape of arrays with a zero size, instead of replacing them by `array([])`, which always has a shape of (0,).\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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CGADnAFtgWdQ8Voo4Ozuzbds2+vXrx5QpU2Jei/79+2Nubs6uXbuoUaMGy5cv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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f8da1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, subplot_kw=dict(projection=ccrs.Mercator()))\n",
    "\n",
    "ds['OUT_EVAP'].mean(dim='time').plot.pcolormesh('lon', 'lat', ax=ax,\n",
    "                                                levels=10, vmin=0, vmax=0.01,\n",
    "                                                transform=ccrs.PlateCarree())\n",
    "\n",
    "# Configure the map\n",
    "gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n",
    "                  linewidth=2, color='gray', alpha=0.5, linestyle='--')\n",
    "ax.set_extent([-125, -118, 47, 49], ccrs.Geodetic())\n",
    "ax.coastlines('10m')\n",
    "gl.xlabels_top = False\n",
    "gl.ylabels_right = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot 6: Plotting domain mean timeseries from VIC Image Driver Output\n",
    "Here, we'll take the domain mean of the downward shortwave radiation and will use pandas to plot the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ce8b160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RB0pPT7Ry2eTJ4Qi5ZeRGaPT2Rq13CxeGI+RZRCuWkQdKIqAi2Qt57n0AzZEbIZGcJ/vv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\nUv0DTYxrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c522588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['OUT_SWDOWN'].mean(dim=('lon', 'lat')).to_dataframe().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot 7: Plotting timeseries at a point from VIC Image Driver Output\n",
    "Here, we'll take a single point of the surface albedo variable and will use pandas to plot the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p1C1fPvDvqlWpsDQqOBttlL5tTZ8+8G+z/mbjp00bm4XLRcUGM39+6mqeeQYee6z5tnr7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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11048e5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds['OUT_ALBEDO'].isel(lat=2, lon=2).plot()\n",
    "plt.ylim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plt.close('all')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
